An effective and efficient truth discovery framework over data streams

Truth discovery, a validity assessment method for conflicting data from various sources, has been widely studied in the conventional database community. However, while existing methods for static scenario involve time-consuming iterative processes, those for streams suffer from much sacrifice on accuracy due to the incremental source weight learning. In this paper, we propose a novel framework to conduct truth discovery over streams, which incorporates various iterative methods to effectively estimate the source weights, and decides the frequency of source weight computation adaptively. Specifically, we first capture the characteristics of source weight evolution, based on which a framework is modeled. Then, we define the conditions of source weight evolution for the situations with relatively small unit and cumulative errors, and construct a probabilistic model that estimates the probability of meeting these conditions. Finally, we propose a novel scheme called adaptive source reliability assessment (ASRA), which converts an estimation problem into an optimization problem. We have conducted extensive experiments over real datasets to prove the high effectiveness and efficiency of our framework.